Mountain View, California

Satvik Sharma

Engineering cloud infrastructure, compute-intensive workloads, CI/CD pipelines, and large-scale distributed platforms.

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500K/min
Sustained peak production throughput
7.8K/sec
Cassandra reads across regions
99.9%
Platform availability achieved
$300M+
Business impact enabled
01 / Profile

Infrastructure engineered for scale, efficiency, and resilience.

Engineer focused on cloud infrastructure, distributed compute, and DevOps. I build scalable platforms and CI/CD pipelines for resource-intensive workloads using Kubernetes, distributed storage, automated delivery, and production reliability practices.

02 / Experience

Feb 2023 — Present

Mountain View, CA

Walmart Global Tech

Software Engineer 3

01

Integrated a real-time computer-vision platform using Java, Spring, Kafka, and WebSockets across 598 stores. The system reduced manual scans by 66%, increased shrink-item identification by 43.1%, and contributed to more than $113M in annual savings.

02

Delivered Tier-0 item and offer platforms across a multi-region environment and more than 30 services. The platform sustained 500K events per minute, supported 7.8K reads and 2.1K writes per second, and achieved 99.9% availability.

May 2022 — Aug 2022

Dallas, TX

Sam’s Club

Software Engineer Intern

01

Engineered high-volume data-migration workflows with Python, Apache Spark, and Redis. Distributed execution and caching improved audit processing speed by 13%.

02

Delivered maintainable production features through Git-based development and Agile collaboration. The workflow emphasized versioned delivery, peer review, and operational reliability.

06 / Research
01

Artificial Intelligence · Healthcare

Artificial Intelligence in Healthcare and Medicine: Clinical Applications, Therapeutic Advances, and Future Perspectives

07 / Travel

Places that have shaped my perspective beyond engineering.

Cities visited across the United States An interactive globe with animated markers for visited cities across the United States.
  1. Los Angeles
  2. San Francisco
  3. New York
  4. Dallas
  5. Bentonville
  6. Little Rock
  7. Salt Lake City
  8. Phoenix
  9. Flagstaff
  10. Miami
  11. Key West
  12. Oklahoma City
  13. San Diego
  14. Chicago
  15. Madison
  16. Las Vegas
  17. Amarillo
  18. Shreveport
  19. South Padre Island
  20. Austin
  21. Houston
  22. Arlington
  23. Spokane
  24. Milwaukee
08 / National Parks
Visited national parks A zoomable United States map marking visited national parks and states.
Visited states
  1. Yosemite National Park
  2. Sequoia National Park
  3. Kings Canyon National Park
  4. Death Valley National Park
  5. Joshua Tree National Park
  6. Zion National Park
  7. Bryce Canyon National Park
  8. Gateway Arch National Park
  9. Glacier National Park
  10. Indiana Dunes National Park
  11. Hot Springs National Park
  12. Big Bend National Park
  13. Carlsbad Caverns National Park
  14. Grand Canyon National Park
  15. Petrified Forest National Park
  16. Guadalupe Mountains National Park
03 / Projects
01GPU infrastructure

Kubernetes GPU-Aware LLM Routing Platform

  • Translates model demand into bounded Kubernetes capacity, batching, queuing, and admission-control plans.
  • 16 model profiles · 1M RPS demand modeling · explicit GPU capacity limits

Kubernetes · Python · GPU Scheduling · FastAPI

View Live ↗View GitHub ↗
02CI/CD & LLMOps

Jenkins LLMOps Delivery Platform

  • Validates, packages, and deploys versioned inference services through automated release gates.
  • Test · scan · publish · deploy · verify · rollback

Jenkins · Docker · Kubernetes · Helm · Prometheus

View Live ↗View GitHub ↗
03Video inference

VisionFlow GPU Video Inference Lab

  • Visualizes how compressed video becomes decoded frames and moves through GPU inference, tracking, analytics, and encoding.
  • GStreamer · DeepStream · TensorRT · CPU/GPU optimization

GStreamer · NVIDIA DeepStream · TensorRT · Kubernetes

View Live ↗View GitHub ↗
NVIDIA worker integration in progress
05 / Infrastructure Stack
01

Compute & Orchestration

Kubernetes, GPU workloads, capacity planning, workload placement, Docker, Azure, GCP

02

Production Reliability

SLOs, SLIs, SLAs, fault tolerance, availability, MTTR, Prometheus, Grafana, incident response

03

Data & Storage

Cassandra, Redis, Cosmos DB, SQL, GPU storage, persistent volumes, distributed data processing

04

Languages & Frameworks

Python, Java, SQL, Spring, FastAPI, Flask, LangChain, Linux/Unix

05

DevOps & CI/CD

CI/CD pipeline engineering, Git, Jenkins, Jira, JFrog, Concord, automated delivery, and release workflows

04 / Education

Master of Computer Science

University of Texas at Arlington

Computer Architecture · Software Engineering Management · Database Systems · Theory of Computation

Jan 2021 — Dec 2022

Bachelor of Computer Science

Amity University

Cloud Computing · Advanced Image Processing · Distributed Systems · Compiler Construction

Aug 2016 — Aug 2020

09 / Contact

Let’s build reliable systems at scale.

Connect with me to discuss cloud infrastructure, compute platforms, distributed systems, and infrastructure for AI workloads.

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